A Review of Digital Innovations for Diet Monitoring
and Precision Nutrition
The central theme of this study is to propose more effective approaches for tracking diets,
considering the limitations of current methods like food diaries and 24-hour recall. These
conventional methods are burdensome and often result in inaccurate estimations of food
consumption. Accurate diet monitoring holds significant importance as it has the potential to
greatly reduce mortality rates and improve overall health through precise nutritional
interventions.
The first recommended method suggests leveraging mobile applications for diet monitoring.
These apps serve as valuable complements to traditional food diaries, as users can
conveniently log their dietary intake using their smartphones. These applications provide
features that offer nutrient information for packaged foods and assist users in making informed
decisions about meal choices and portion sizes. One notable advantage of these apps is the
ability to track meals through photos, ensuring both convenience and accuracy. Users can
capture real-time images of their meals, eliminating the need to rely on memory or enter multiple
entries. Furthermore, the integration of photo diaries with artificial intelligence (AI) and
continuous glucose monitoring (CGM) technologies allows for the identification of specific foods
that may cause undesirable glucose spikes.
The second proposed method involves the utilization of sensors to enhance the tracking of
eating habits and nutritional intake, thereby increasing accuracy while reducing user burden.
Two primary types of sensors are involved: physical and chemical sensors. Physical sensors
detect specific gestures associated with eating, such as hand-to-mouth movements. While
wearable sensors on wrists may be limited to laboratory settings, additional sensors like
electromyography, piezoelectric, and acoustic sensors can be used to monitor muscle
movements in the jaw, identifying chewing and swallowing sounds. Chemical sensors, on the
other hand, employ dietary biomarkers to track nutritional content. Continuous Glucose
Monitoring (CGM) is an example of such a biomarker, utilizing changes in blood glucose levels
following a meal to provide insight into the macronutrient composition of consumed food.
Through the application of CGM and machine learning, a study successfully developed a model
capable of accurately predicting macronutrient components, showing promising results despite
variations in individual food metabolism. Additionally, ketones derived from breath analysis
serve as another biomarker, enabling the assessment of ketosis for individuals on ketogenic
diets or individuals with diabetes at risk of ketoacidosis. Ongoing research is exploring the use
of sweat and saliva to track nutritional components, although these approaches are still in the
experimental stage.
The final proposed method involves utilizing technology to develop personalized nutrition
programs based on measurements of gut microbiome and blood glucose levels. A study
conducted in this field focused on developing a machine learning model that utilizes CGM data
to predict individual glucose responses to meals, incorporating factors such as microbiome
composition and blood panel results. Companies can utilize the data generated by this model to
formulate personalized nutrition programs, empowering users to effectively combat diabetes and
other metabolic diseases.
Overall, limitations include decreasing adherence over time, even with low-burden tools, and the
potential impact on in-the-moment awareness, which can hinder weight loss. Another concern
arises with personalized nutrition programs based on CGM, as measurements can vary across
different devices. These limitations highlight the importance of engaging behavior-modification
researchers to design interventions that promote adherence and lifestyle modification.
Modeling Individual Differences in Food
Metabolism through Altering Least Squares
The motive of the study was to get a better understanding of the effects of macronutrients on
blood glucose level, known as postprandial glucose response (PPGR). The blood glucose level
is not only dependent on carbohydrates, but also dependent on protein, fat, and inter-individual
differences in macronutrient metabolism. Understanding PPGR is crucial to real-world
applications such as developing personalized nutrition programs and automatically monitoring
diet using CGM.
Due to the inter-individual differences in macronutrient metabolism, individuals have different
PPGR after having the same meal. Previous works include using the shape of PPGR to predict
the meal macronutrients’ amounts with a machine learning model, which was pretty successful
given that the models were not customized for each participant. Another study built a machine
learning model to predict PPGR based on “phenotypes” such as blood panels and gut
microbiota.
This research aims to learn how each macronutrient contributes to PPGR and capture the
unique PPGR of each individual and use this information as a scaling factor for the impact of
macronutrients. The researchers came up with an equation, X = A𝛼Z, where X is the PPGR, A is
the basis function (the impact of macronutrients), 𝛼 is the sensitivity variable (varies by
individual), and Z is macronutrients stored in each meal. They use a technique called altering
least squares (ALS) to solve for A and 𝛼. They first assume 𝛼=1 to to estimate the matrix A, then
solve for 𝛼 for each individual with value of A.
Researchers discovered that increasing carbohydrates results in higher and prolonged PPGR
peak while increasing protein and fat reduces the PPGR peak while making it more sustained.
They also discovered that there is a wide range of sensitivity variables among the subject pool,
meaning a large inter-individual difference in macronutrient metabolism, which makes the model
based on averages rather limited. The model accounting for these sensitivity variables produced
more effective results as for example, a subject who is more sensitive to fat has a weighted fat
sensitivity variable for more accurate results of PPGR.
The limitations of this research include an assumption that the macronutrients’ effects are linear
and additive, which can be complemented in the future work where product terms and
nonlinearities can be taken into account. Another direction for future research is using the
sensitivity parameters to generate “synthetic patients” and develop personalized diet
recommendations that reduce high glucose excursions after a meal.
Towards the Development of Subject-independent
Inverse Metabolic Models
Diet monitoring is crucial in managing type 2 diabetes and other dietary diseases; however,
traditional ways of monitoring diet are often time consuming and inaccurate. Using CGMs to
automatically monitor dietary intake and using PPGR to estimate the macronutrient components
of a meal could be more convenient and effective in managing the issue. Therefore, the
researchers attempted to develop inverse metabolic models (IMM) to estimate the macronutrient
components of a meal based on the shape of the PPGR.
A lot of research has been done in terms of predicting the PPGR given the meal’s macronutrient
components, but not vice versa. Previous research has discovered that there are huge
inter-individual differences in the glucose response to a meal, which poses a great challenge
when developing IMMs. They have developed machine learning models to predict the PPGR
based on individual’s phenotypes such as blood panels and gut microbiota.
The research used the signal processing methods which contain 4 steps: data preprocessing,
feature extraction, standardization, and model training. First, they preprocessed raw PPGRs
with a Kalman filter to denoise the signal and handle missing values. Next, they extracted
features to capture PPGR shapes using Gaussian kernels. Next, they applied the
standardization methods to reduce individual differences in PPGRs when training
subject-independent models using three complementary approaches: baseline correction
(accounts for the pre-meal glucose level), feature normalization (scale the range of a feature
space relative to the minima/maxima of the data or their mean/standard deviation), and
personalization (account for body composition of each person). Finally, they trained an IMM
using gradient descent boosting (XGBoost) vs linear regression (LR) in order to predict the
macronutrient composition of the meals from the resulting features.
To evaluate the method, the researchers first compared XGBoost against LR without
standardization; XGBoost outperformed LR, implying higher-dimensional and non-linear
relationships between macronutrients and PPGRs. All three standardization methods
complemented the inter-individual variability. For baseline correction, subtraction yielded better
results than division. Feature-wise normalization improved carbohydrates and fat predictions
over curve-wise normalization while protein predictions saw marginal improvements. For
personalization, the researchers discovered that scaling the target values by BMI enhances
both the correlation and RMSRE for all macronutrients.
Limitations of the research include the controlled setting of the study, where participants were
inactive after eating. This means that the model did not consider the impact of physical activity
on lowering postprandial glucose levels. Therefore, future work is necessary to assess the
effectiveness of our approach in more realistic environments and with a wider range of meal
types. Currently, ongoing efforts involve conducting experiments where participants consume
multiple solid and liquid meals throughout an extended period while engaging in their usual daily
activities.